kmeans_model_new

KMeans_model.ipynb

This script trains a KMeans model on student data, performs clustering, and finds matching students based on input data. Automatically generated by Colab.

Original file is located at https://colab.research.google.com/drive/1NdtQgxHskYoeVWsczWuXAYcstjXB9xLO

  1# -*- coding: utf-8 -*-
  2"""KMeans_model.ipynb
  3
  4This script trains a KMeans model on student data, performs clustering, and finds matching students based on input data.
  5Automatically generated by Colab.
  6
  7Original file is located at
  8    https://colab.research.google.com/drive/1NdtQgxHskYoeVWsczWuXAYcstjXB9xLO
  9"""
 10
 11import json
 12import numpy as np
 13import pandas as pd
 14from sklearn.cluster import KMeans
 15from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler
 16from sklearn.impute import SimpleImputer
 17from sklearn.metrics import pairwise_distances
 18import joblib  # For saving the model
 19
 20def load_json_data(file_path):
 21    """
 22    Load student data from a JSON file.
 23
 24    Args:
 25        file_path (str): Path to the JSON file containing student data.
 26
 27    Returns:
 28        list: List of student data as dictionaries.
 29    """
 30    with open(file_path, 'r') as file:
 31        data = json.load(file)
 32    return data
 33
 34# Load student data from the JSON file
 35file_path = 'students_large_dataset.json'  # Replace with your file path
 36students = load_json_data(file_path)
 37
 38# Convert JSON data to a pandas DataFrame
 39student_df = pd.DataFrame(students)
 40
 41# Standardize subject names to lowercase for uniformity
 42student_df['subjects'] = student_df['subjects'].apply(lambda x: [s.lower() for s in x])
 43
 44# Encode subjects using MultiLabelBinarizer
 45mlb = MultiLabelBinarizer()
 46subjects_encoded = mlb.fit_transform(student_df["subjects"])
 47
 48# Calculate average study hours for each student
 49avg_study_hours = student_df["study_hours"].apply(np.mean).values.reshape(-1, 1)
 50
 51# Encode goals using a predefined mapping (example mapping shown)
 52goal_mapping = {
 53    "Exam preparation": 0,
 54    "Assignments": 1,
 55    "Concept review": 2,
 56    "Project work": 3,
 57    "Homework": 4,
 58    "Practice": 5
 59}
 60goals_encoded = student_df["goals"].map(goal_mapping).values.reshape(-1, 1)
 61
 62# Combine encoded subjects, average study hours, and goals for each student
 63features = np.hstack([subjects_encoded, avg_study_hours, goals_encoded])
 64
 65# Impute missing values in the features using the mean of each column
 66imputer = SimpleImputer(strategy="mean")
 67features_imputed = imputer.fit_transform(features)
 68
 69# Scale the imputed features to have zero mean and unit variance
 70scaler = StandardScaler()
 71features_scaled = scaler.fit_transform(features_imputed)
 72
 73# Train the KMeans model to cluster students into 3 groups
 74kmeans = KMeans(n_clusters=3, random_state=42)
 75student_df["cluster"] = kmeans.fit_predict(features_scaled)
 76
 77# Save the trained KMeans model, scaler, imputer, and MultiLabelBinarizer for later use
 78joblib.dump(kmeans, 'kmeans_model.pkl')
 79joblib.dump(scaler, 'scaler.pkl')
 80joblib.dump(imputer, 'imputer.pkl')
 81joblib.dump(mlb, 'mlb.pkl')
 82
 83print("KMeans model, scaler, and MultiLabelBinarizer have been saved.")
 84
 85def find_matching_students(new_student, student_df, kmeans, scaler, mlb, n_matches=3):
 86    """
 87    Find the top matching students for a new student based on their study profile.
 88
 89    Args:
 90        new_student (dict): The new student's data (name, subjects, study hours, goals).
 91        student_df (pd.DataFrame): DataFrame containing the student data.
 92        kmeans (KMeans): Trained KMeans model to predict the cluster for the new student.
 93        scaler (StandardScaler): Pre-trained scaler to standardize the features.
 94        mlb (MultiLabelBinarizer): Pre-trained MultiLabelBinarizer to encode subjects.
 95        n_matches (int): The number of closest matching students to return (default is 3).
 96
 97    Returns:
 98        pd.DataFrame: The `n_matches` closest students from the same cluster.
 99    """
100    # Process new student data
101    new_subjects = mlb.transform([[s.lower() for s in new_student["subjects"]]])[0]
102    new_avg_hour = np.mean(new_student["study_hours"])
103    new_goal_encoded = goal_mapping[new_student["goals"]]
104
105    # Create a feature vector for the new student
106    new_student_features = np.hstack([new_subjects, new_avg_hour, new_goal_encoded]).reshape(1, -1)
107    new_student_features_scaled = scaler.transform(new_student_features)
108
109    # Predict the cluster for the new student
110    cluster = kmeans.predict(new_student_features_scaled)[0]
111
112    # Filter the students to those in the same cluster
113    cluster_students = student_df[student_df["cluster"] == cluster]
114    cluster_features = features_scaled[student_df["cluster"] == cluster]
115
116    # Calculate pairwise Euclidean distances to find the closest matches
117    distances = pairwise_distances(new_student_features_scaled, cluster_features, metric="euclidean").flatten()
118    closest_indices = distances.argsort()[:n_matches]
119
120    # Return the closest matching students
121    return cluster_students.iloc[closest_indices]
122
123# Append a new student and find matching students
124new_student = {
125    "name": "KULDEEP",
126    "subjects": ["math", "biology"],
127    "study_hours": [12, 17],
128    "goals": "Exam preparation"
129}
130
131# Load previously saved models and encoders
132kmeans_model = joblib.load('kmeans_model.pkl')
133scaler = joblib.load('scaler.pkl')
134mlb = joblib.load('mlb.pkl')
135
136# Find the top 5 matching students for the new student
137matching_students = find_matching_students(new_student, student_df, kmeans_model, scaler, mlb, n_matches=5)
138
139# Print the top matching students' details
140print("Top matching students:")
141print(matching_students[["name", "subjects", "study_hours", "goals"]])
142
143# Append the new student to the dataset and save it back to the JSON file
144students.append(new_student)
145with open(file_path, 'w') as file:
146    json.dump(students, file, indent=4)
147
148# Reload data and prepare features for clustering
149student_df = pd.DataFrame(students)
150student_df['subjects'] = student_df['subjects'].apply(lambda x: [s.lower() for s in x])
151subjects_encoded = mlb.fit_transform(student_df["subjects"])
152avg_study_hours = student_df["study_hours"].apply(np.mean).values.reshape(-1, 1)
153goals_encoded = student_df["goals"].map(goal_mapping).values.reshape(-1, 1)
154
155# Impute and scale features
156features = np.hstack([subjects_encoded, avg_study_hours, goals_encoded])
157imputer = SimpleImputer(strategy="mean")
158features_imputed = imputer.fit_transform(features)  # Impute NaNs
159
160scaler = StandardScaler()
161features_scaled = scaler.fit_transform(features_imputed)  # Scale the imputed features
162
163# Train and save the updated KMeans model
164kmeans = KMeans(n_clusters=3, random_state=42)
165student_df["cluster"] = kmeans.fit_predict(features_scaled)
166joblib.dump(kmeans, 'kmeans_model.pkl')
167joblib.dump(scaler, 'scaler.pkl')
168joblib.dump(imputer, 'imputer.pkl')
169joblib.dump(mlb, 'mlb.pkl')
170
171print("Retrained model and updated data have been saved.")
def load_json_data(file_path):
21def load_json_data(file_path):
22    """
23    Load student data from a JSON file.
24
25    Args:
26        file_path (str): Path to the JSON file containing student data.
27
28    Returns:
29        list: List of student data as dictionaries.
30    """
31    with open(file_path, 'r') as file:
32        data = json.load(file)
33    return data

Load student data from a JSON file.

Args: file_path (str): Path to the JSON file containing student data.

Returns: list: List of student data as dictionaries.

file_path = 'students_large_dataset.json'
students = [{'name': 'Aadi', 'subjects': ['math', 'chemistry', 'english', 'biology'], 'study_hours': [15, 19], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Advait', 'subjects': ['chemistry', 'computer science'], 'study_hours': [5, 8], 'goals': 'Homework', 'cluster': 0}, {'name': 'Akhil', 'subjects': ['english', 'math'], 'study_hours': [10, 13], 'goals': 'Project work', 'cluster': 0}, {'name': 'Ansh', 'subjects': ['history'], 'study_hours': [15, 17], 'goals': 'Homework', 'cluster': 1}, {'name': 'Parth', 'subjects': ['english', 'math', 'computer science', 'geography'], 'study_hours': [10, 14], 'goals': 'Practice', 'cluster': 0}, {'name': 'Aaditya', 'subjects': ['computer science'], 'study_hours': [14, 17], 'goals': 'Practice', 'cluster': 1}, {'name': 'Arjun', 'subjects': ['biology', 'physics'], 'study_hours': [9, 13], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Sai', 'subjects': ['computer science', 'math', 'chemistry'], 'study_hours': [18, 19], 'goals': 'Homework', 'cluster': 0}, {'name': 'Rohan', 'subjects': ['english', 'biology'], 'study_hours': [9, 13], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Samarth', 'subjects': ['english', 'physics'], 'study_hours': [6, 7], 'goals': 'Homework', 'cluster': 2}, {'name': 'Aaditya', 'subjects': ['computer science', 'math', 'geography'], 'study_hours': [6, 7], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Akash', 'subjects': ['english', 'computer science', 'physics', 'history'], 'study_hours': [17, 20], 'goals': 'Project work', 'cluster': 2}, {'name': 'Ishan', 'subjects': ['physics', 'chemistry', 'english'], 'study_hours': [8, 11], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Shaurya', 'subjects': ['english'], 'study_hours': [10, 12], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Atharv', 'subjects': ['geography', 'computer science', 'math'], 'study_hours': [6, 8], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Samarth', 'subjects': ['math'], 'study_hours': [10, 12], 'goals': 'Homework', 'cluster': 0}, {'name': 'Samarth', 'subjects': ['geography'], 'study_hours': [7, 8], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Avi', 'subjects': ['biology'], 'study_hours': [10, 11], 'goals': 'Project work', 'cluster': 1}, {'name': 'Aryan', 'subjects': ['math'], 'study_hours': [17, 18], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Arjun', 'subjects': ['physics', 'history'], 'study_hours': [15, 19], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Vihaan', 'subjects': ['math', 'english', 'computer science', 'biology'], 'study_hours': [7, 10], 'goals': 'Practice', 'cluster': 1}, {'name': 'Parth', 'subjects': ['biology', 'geography'], 'study_hours': [10, 12], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Rohan', 'subjects': ['physics', 'english', 'biology'], 'study_hours': [5, 9], 'goals': 'Practice', 'cluster': 2}, {'name': 'Kiaan', 'subjects': ['history', 'biology', 'computer science'], 'study_hours': [14, 15], 'goals': 'Project work', 'cluster': 1}, {'name': 'Amrit', 'subjects': ['history'], 'study_hours': [9, 12], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Shaurya', 'subjects': ['history', 'english', 'chemistry', 'computer science'], 'study_hours': [10, 13], 'goals': 'Project work', 'cluster': 0}, {'name': 'Amrit', 'subjects': ['physics', 'math', 'english', 'computer science'], 'study_hours': [14, 16], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Arham', 'subjects': ['geography', 'history', 'computer science', 'english'], 'study_hours': [13, 17], 'goals': 'Practice', 'cluster': 1}, {'name': 'Samarth', 'subjects': ['math', 'physics', 'biology', 'computer science'], 'study_hours': [18, 20], 'goals': 'Project work', 'cluster': 2}, {'name': 'Raghav', 'subjects': ['math', 'computer science', 'biology', 'chemistry'], 'study_hours': [16, 17], 'goals': 'Project work', 'cluster': 0}, {'name': 'Arnav', 'subjects': ['chemistry'], 'study_hours': [8, 12], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Aarav', 'subjects': ['math', 'history'], 'study_hours': [10, 11], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Ayaan', 'subjects': ['english', 'chemistry'], 'study_hours': [5, 9], 'goals': 'Project work', 'cluster': 0}, {'name': 'Arham', 'subjects': ['math'], 'study_hours': [16, 17], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Krishna', 'subjects': ['physics', 'geography', 'computer science'], 'study_hours': [13, 14], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Aryan', 'subjects': ['history', 'chemistry', 'english'], 'study_hours': [10, 14], 'goals': 'Practice', 'cluster': 0}, {'name': 'Aayush', 'subjects': ['geography', 'chemistry', 'english', 'physics'], 'study_hours': [8, 11], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Krishna', 'subjects': ['english', 'chemistry'], 'study_hours': [15, 16], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Anuj', 'subjects': ['math'], 'study_hours': [14, 16], 'goals': 'Homework', 'cluster': 0}, {'name': 'Kartik', 'subjects': ['geography', 'english', 'physics'], 'study_hours': [13, 17], 'goals': 'Practice', 'cluster': 2}, {'name': 'Shaurya', 'subjects': ['english'], 'study_hours': [9, 10], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Vihan', 'subjects': ['english', 'biology', 'math'], 'study_hours': [9, 13], 'goals': 'Project work', 'cluster': 1}, {'name': 'Rohan', 'subjects': ['physics', 'biology', 'math'], 'study_hours': [16, 17], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Rohan', 'subjects': ['chemistry', 'history'], 'study_hours': [18, 19], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Sai', 'subjects': ['biology', 'math'], 'study_hours': [13, 14], 'goals': 'Project work', 'cluster': 1}, {'name': 'Ansh', 'subjects': ['computer science', 'biology', 'english'], 'study_hours': [8, 9], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Om', 'subjects': ['chemistry', 'biology', 'physics'], 'study_hours': [13, 14], 'goals': 'Practice', 'cluster': 2}, {'name': 'Vivaan', 'subjects': ['biology', 'math', 'computer science', 'history'], 'study_hours': [14, 15], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Shivansh', 'subjects': ['physics', 'computer science'], 'study_hours': [16, 19], 'goals': 'Homework', 'cluster': 2}, {'name': 'Ishaan', 'subjects': ['computer science'], 'study_hours': [9, 13], 'goals': 'Practice', 'cluster': 1}, {'name': 'Ajay', 'subjects': ['computer science', 'history', 'chemistry', 'biology'], 'study_hours': [18, 20], 'goals': 'Homework', 'cluster': 1}, {'name': 'Shivansh', 'subjects': ['math', 'chemistry'], 'study_hours': [15, 16], 'goals': 'Homework', 'cluster': 0}, {'name': 'Ishaan', 'subjects': ['chemistry', 'geography', 'biology'], 'study_hours': [18, 22], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Sai', 'subjects': ['physics'], 'study_hours': [6, 7], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Aarav', 'subjects': ['history', 'geography', 'physics', 'computer science'], 'study_hours': [15, 19], 'goals': 'Homework', 'cluster': 2}, {'name': 'Divit', 'subjects': ['physics'], 'study_hours': [10, 14], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Kartik', 'subjects': ['computer science', 'geography', 'biology', 'english'], 'study_hours': [5, 6], 'goals': 'Practice', 'cluster': 1}, {'name': 'Ritvik', 'subjects': ['english', 'chemistry', 'math'], 'study_hours': [15, 18], 'goals': 'Project work', 'cluster': 0}, {'name': 'Aadi', 'subjects': ['math', 'physics', 'history', 'geography'], 'study_hours': [7, 10], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Aditya', 'subjects': ['geography', 'physics', 'biology', 'english'], 'study_hours': [10, 12], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Kiaan', 'subjects': ['computer science'], 'study_hours': [16, 17], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Parth', 'subjects': ['math', 'computer science'], 'study_hours': [10, 13], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Akash', 'subjects': ['biology', 'computer science', 'geography'], 'study_hours': [5, 9], 'goals': 'Homework', 'cluster': 1}, {'name': 'Arham', 'subjects': ['physics', 'history', 'geography'], 'study_hours': [14, 18], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Amrit', 'subjects': ['biology'], 'study_hours': [8, 12], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Ankit', 'subjects': ['computer science', 'chemistry'], 'study_hours': [5, 6], 'goals': 'Project work', 'cluster': 0}, {'name': 'Rudra', 'subjects': ['biology'], 'study_hours': [5, 9], 'goals': 'Homework', 'cluster': 1}, {'name': 'Kabir', 'subjects': ['chemistry'], 'study_hours': [17, 21], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Reyansh', 'subjects': ['physics', 'math', 'biology', 'computer science'], 'study_hours': [8, 12], 'goals': 'Practice', 'cluster': 2}, {'name': 'Kiaan', 'subjects': ['computer science'], 'study_hours': [18, 21], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Yash', 'subjects': ['physics'], 'study_hours': [18, 19], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Yash', 'subjects': ['math', 'geography'], 'study_hours': [7, 8], 'goals': 'Homework', 'cluster': 0}, {'name': 'Ishaan', 'subjects': ['math', 'physics'], 'study_hours': [10, 11], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Dev', 'subjects': ['biology'], 'study_hours': [6, 10], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Parth', 'subjects': ['english', 'physics', 'math'], 'study_hours': [7, 10], 'goals': 'Project work', 'cluster': 2}, {'name': 'Kartik', 'subjects': ['history', 'physics', 'english'], 'study_hours': [5, 8], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Aadi', 'subjects': ['chemistry', 'geography'], 'study_hours': [7, 9], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Ankit', 'subjects': ['english', 'chemistry'], 'study_hours': [18, 19], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Avi', 'subjects': ['geography'], 'study_hours': [10, 11], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Kiaan', 'subjects': ['geography', 'history', 'physics'], 'study_hours': [15, 18], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Shaurya', 'subjects': ['history', 'computer science', 'english', 'math'], 'study_hours': [16, 17], 'goals': 'Project work', 'cluster': 0}, {'name': 'Amit', 'subjects': ['computer science', 'physics', 'chemistry'], 'study_hours': [16, 17], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Reyansh', 'subjects': ['physics', 'english', 'geography'], 'study_hours': [10, 14], 'goals': 'Project work', 'cluster': 2}, {'name': 'Ritvik', 'subjects': ['geography', 'physics', 'computer science', 'biology'], 'study_hours': [17, 19], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Siddharth', 'subjects': ['computer science'], 'study_hours': [7, 8], 'goals': 'Practice', 'cluster': 1}, {'name': 'Pranav', 'subjects': ['math', 'physics', 'chemistry', 'computer science'], 'study_hours': [17, 19], 'goals': 'Project work', 'cluster': 0}, {'name': 'Akhil', 'subjects': ['physics', 'history', 'biology', 'computer science'], 'study_hours': [15, 16], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Rishi', 'subjects': ['math'], 'study_hours': [16, 20], 'goals': 'Project work', 'cluster': 0}, {'name': 'Reyansh', 'subjects': ['history', 'chemistry', 'computer science'], 'study_hours': [9, 10], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Arjun', 'subjects': ['english', 'computer science', 'chemistry'], 'study_hours': [9, 11], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Arjun', 'subjects': ['english', 'chemistry', 'computer science', 'physics'], 'study_hours': [9, 10], 'goals': 'Project work', 'cluster': 2}, {'name': 'Arham', 'subjects': ['english', 'chemistry', 'biology', 'geography'], 'study_hours': [6, 7], 'goals': 'Practice', 'cluster': 1}, {'name': 'Ayaan', 'subjects': ['geography', 'english', 'biology', 'physics'], 'study_hours': [15, 19], 'goals': 'Practice', 'cluster': 2}, {'name': 'Kiaan', 'subjects': ['computer science', 'biology'], 'study_hours': [16, 17], 'goals': 'Project work', 'cluster': 1}, {'name': 'Kabir', 'subjects': ['computer science', 'math'], 'study_hours': [5, 8], 'goals': 'Project work', 'cluster': 0}, {'name': 'Vihan', 'subjects': ['physics'], 'study_hours': [10, 14], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Dev', 'subjects': ['physics'], 'study_hours': [7, 10], 'goals': 'Practice', 'cluster': 2}, {'name': 'Om', 'subjects': ['english', 'physics'], 'study_hours': [8, 12], 'goals': 'Practice', 'cluster': 2}, {'name': 'Rohan', 'subjects': ['chemistry', 'computer science', 'biology', 'history'], 'study_hours': [8, 10], 'goals': 'Homework', 'cluster': 1}, {'name': 'Laksh', 'subjects': ['biology', 'physics'], 'study_hours': [15, 19], 'goals': 'Project work', 'cluster': 2}, {'name': 'Ishan', 'subjects': ['biology', 'geography', 'physics', 'computer science'], 'study_hours': [9, 10], 'goals': 'Project work', 'cluster': 2}, {'name': 'Aditya', 'subjects': ['physics', 'geography'], 'study_hours': [7, 8], 'goals': 'Practice', 'cluster': 2}, {'name': 'Arham', 'subjects': ['biology', 'english'], 'study_hours': [17, 18], 'goals': 'Practice', 'cluster': 1}, {'name': 'Divit', 'subjects': ['history', 'biology', 'geography'], 'study_hours': [10, 14], 'goals': 'Homework', 'cluster': 1}, {'name': 'Siddharth', 'subjects': ['english', 'history', 'biology', 'physics'], 'study_hours': [15, 16], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Vihaan', 'subjects': ['history', 'math'], 'study_hours': [18, 19], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Arjun', 'subjects': ['biology', 'english', 'computer science', 'geography'], 'study_hours': [8, 10], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Avi', 'subjects': ['physics'], 'study_hours': [15, 19], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Rudra', 'subjects': ['math'], 'study_hours': [7, 8], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Raghav', 'subjects': ['biology', 'geography', 'physics'], 'study_hours': [18, 21], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Raghav', 'subjects': ['english', 'biology', 'chemistry', 'physics'], 'study_hours': [7, 8], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Siddharth', 'subjects': ['computer science', 'math', 'biology', 'history'], 'study_hours': [16, 17], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Sai', 'subjects': ['physics', 'geography'], 'study_hours': [8, 10], 'goals': 'Project work', 'cluster': 2}, {'name': 'Shivansh', 'subjects': ['geography'], 'study_hours': [13, 15], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Ishan', 'subjects': ['chemistry', 'geography'], 'study_hours': [15, 18], 'goals': 'Project work', 'cluster': 0}, {'name': 'Reyansh', 'subjects': ['biology', 'history', 'chemistry'], 'study_hours': [17, 21], 'goals': 'Homework', 'cluster': 1}, {'name': 'Arham', 'subjects': ['english', 'physics', 'math'], 'study_hours': [9, 10], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Amit', 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'study_hours': [10, 12], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Akhil', 'subjects': ['geography', 'chemistry'], 'study_hours': [7, 8], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Reyansh', 'subjects': ['biology', 'english', 'history'], 'study_hours': [9, 12], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Aayush', 'subjects': ['english', 'computer science', 'geography', 'history'], 'study_hours': [13, 16], 'goals': 'Homework', 'cluster': 1}, {'name': 'Yash', 'subjects': ['biology'], 'study_hours': [10, 12], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Aayush', 'subjects': ['biology', 'computer science'], 'study_hours': [5, 8], 'goals': 'Practice', 'cluster': 1}, {'name': 'Arjun', 'subjects': ['biology'], 'study_hours': [18, 21], 'goals': 'Project work', 'cluster': 1}, {'name': 'Ankit', 'subjects': ['english', 'history', 'biology', 'geography'], 'study_hours': [9, 12], 'goals': 'Homework', 'cluster': 1}, {'name': 'Akash', 'subjects': ['computer science', 'history', 'biology'], 'study_hours': [14, 18], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Krishna', 'subjects': ['biology', 'english'], 'study_hours': [15, 18], 'goals': 'Practice', 'cluster': 1}, {'name': 'Ansh', 'subjects': ['history', 'computer science', 'geography', 'biology'], 'study_hours': [8, 11], 'goals': 'Practice', 'cluster': 1}, {'name': 'Shaurya', 'subjects': ['history', 'english'], 'study_hours': [5, 8], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Sai', 'subjects': ['chemistry'], 'study_hours': [15, 16], 'goals': 'Practice', 'cluster': 0}, {'name': 'Ishan', 'subjects': ['biology', 'english', 'computer science'], 'study_hours': [15, 17], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Arnav', 'subjects': ['biology'], 'study_hours': [14, 17], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Darsh', 'subjects': ['physics', 'math', 'geography'], 'study_hours': [8, 9], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Aaditya', 'subjects': ['english'], 'study_hours': [15, 16], 'goals': 'Practice', 'cluster': 1}, {'name': 'Reyansh', 'subjects': ['chemistry', 'history'], 'study_hours': [16, 17], 'goals': 'Practice', 'cluster': 0}, {'name': 'Aryan', 'subjects': ['math'], 'study_hours': [10, 14], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Arjun', 'subjects': ['math'], 'study_hours': [17, 20], 'goals': 'Practice', 'cluster': 0}, {'name': 'Ayaan', 'subjects': ['physics'], 'study_hours': [15, 19], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Vivaan', 'subjects': ['computer science', 'physics', 'chemistry', 'biology'], 'study_hours': [5, 8], 'goals': 'Practice', 'cluster': 2}, {'name': 'Reyansh', 'subjects': ['geography'], 'study_hours': [10, 14], 'goals': 'Homework', 'cluster': 1}, {'name': 'Ansh', 'subjects': ['math', 'biology', 'physics', 'computer science'], 'study_hours': [9, 13], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Ritvik', 'subjects': ['biology', 'chemistry', 'math'], 'study_hours': [18, 20], 'goals': 'Practice', 'cluster': 0}, {'name': 'Aadi', 'subjects': ['geography'], 'study_hours': [15, 19], 'goals': 'Project work', 'cluster': 1}, {'name': 'Rudra', 'subjects': ['computer science', 'math', 'english'], 'study_hours': [17, 19], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Aarav', 'subjects': ['chemistry'], 'study_hours': [6, 9], 'goals': 'Practice', 'cluster': 0}, {'name': 'Kiaan', 'subjects': ['chemistry', 'physics', 'computer science'], 'study_hours': [9, 11], 'goals': 'Homework', 'cluster': 2}, {'name': 'Reyansh', 'subjects': ['computer science', 'math', 'history'], 'study_hours': [7, 10], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Kiaan', 'subjects': ['math', 'english', 'physics', 'computer science'], 'study_hours': [7, 9], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Amit', 'subjects': ['english'], 'study_hours': [6, 10], 'goals': 'Practice', 'cluster': 1}, {'name': 'Shivansh', 'subjects': ['chemistry', 'biology'], 'study_hours': [14, 18], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'Krishna', 'subjects': ['english'], 'study_hours': [15, 18], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Ayaan', 'subjects': ['biology', 'physics', 'geography', 'history'], 'study_hours': [14, 15], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Divit', 'subjects': ['geography'], 'study_hours': [9, 13], 'goals': 'Practice', 'cluster': 1}, {'name': 'Kartik', 'subjects': ['math'], 'study_hours': [14, 16], 'goals': 'Concept review', 'cluster': 0}, {'name': 'Ajay', 'subjects': ['biology', 'chemistry'], 'study_hours': [18, 20], 'goals': 'Project work', 'cluster': 1}, {'name': 'Kartik', 'subjects': ['computer science', 'physics', 'chemistry', 'biology'], 'study_hours': [9, 10], 'goals': 'Project work', 'cluster': 2}, {'name': 'Anuj', 'subjects': ['computer science', 'geography', 'chemistry', 'english'], 'study_hours': [6, 7], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Akash', 'subjects': ['geography'], 'study_hours': [18, 20], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Avi', 'subjects': ['math'], 'study_hours': [5, 6], 'goals': 'Homework', 'cluster': 0}, {'name': 'Ankit', 'subjects': ['chemistry', 'history', 'geography'], 'study_hours': [15, 17], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'Ankit', 'subjects': ['geography', 'math', 'computer science'], 'study_hours': [5, 7], 'goals': 'Assignments', 'cluster': 0}, {'name': 'Aryan', 'subjects': ['math', 'english', 'computer science', 'biology'], 'study_hours': [5, 9], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Amrit', 'subjects': ['english', 'physics'], 'study_hours': [9, 12], 'goals': 'Assignments', 'cluster': 2}, {'name': 'Aaryan', 'subjects': ['physics', 'geography', 'computer science', 'chemistry'], 'study_hours': [16, 18], 'goals': 'Concept review', 'cluster': 2}, {'name': 'Ansh', 'subjects': ['history', 'math', 'biology'], 'study_hours': [6, 8], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Atharv', 'subjects': ['english'], 'study_hours': [18, 19], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Ajay', 'subjects': ['physics', 'history', 'computer science'], 'study_hours': [17, 20], 'goals': 'Practice', 'cluster': 2}, {'name': 'Aadi', 'subjects': ['biology', 'chemistry', 'physics', 'computer science'], 'study_hours': [14, 16], 'goals': 'Homework', 'cluster': 2}, {'name': 'Divit', 'subjects': ['english', 'computer science', 'history'], 'study_hours': [17, 19], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Aditya', 'subjects': ['geography', 'history'], 'study_hours': [17, 18], 'goals': 'Concept review', 'cluster': 1}, {'name': 'Arjun', 'subjects': ['biology', 'english'], 'study_hours': [6, 7], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Avi', 'subjects': ['physics'], 'study_hours': [5, 9], 'goals': 'Practice', 'cluster': 2}, {'name': 'Om', 'subjects': ['physics', 'english'], 'study_hours': [7, 11], 'goals': 'Exam preparation', 'cluster': 2}, {'name': 'Aadi', 'subjects': ['computer science'], 'study_hours': [15, 16], 'goals': 'Assignments', 'cluster': 1}, {'name': 'Akhil', 'subjects': ['math'], 'study_hours': [14, 17], 'goals': 'Practice', 'cluster': 0}, {'name': 'ssingh24', 'subjects': ['math'], 'study_hours': [14, 17], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'ssingh24', 'subjects': ['math'], 'study_hours': [14, 17], 'goals': 'Exam preparation', 'cluster': 0}, {'name': 'KULDEEP', 'subjects': ['math', 'biology'], 'study_hours': [12, 17], 'goals': 'Exam preparation', 'cluster': 1}, {'name': 'KULDEEP', 'subjects': ['math', 'biology'], 'study_hours': [12, 17], 'goals': 'Exam preparation'}]
student_df = name subjects study_hours goals cluster 0 Aadi [math, chemistry, english, biology] [15, 19] Concept review 0 1 Advait [chemistry, computer science] [5, 8] Homework 1 2 Akhil [english, math] [10, 13] Project work 0 3 Ansh [history] [15, 17] Homework 2 4 Parth [english, math, computer science, geography] [10, 14] Practice 0 ... ... ... ... ... ... 49999 Akhil [math] [14, 17] Practice 0 50000 ssingh24 [math] [14, 17] Exam preparation 0 50001 ssingh24 [math] [14, 17] Exam preparation 0 50002 KULDEEP [math, biology] [12, 17] Exam preparation 0 50003 KULDEEP [math, biology] [12, 17] Exam preparation 0 [50004 rows x 5 columns]
mlb = MultiLabelBinarizer()
subjects_encoded = array([[1, 1, 0, ..., 0, 1, 0], [0, 1, 1, ..., 0, 0, 0], [0, 0, 0, ..., 0, 1, 0], ..., [0, 0, 0, ..., 0, 1, 0], [1, 0, 0, ..., 0, 1, 0], [1, 0, 0, ..., 0, 1, 0]])
avg_study_hours = array([[17. ], [ 6.5], [11.5], ..., [15.5], [14.5], [14.5]])
goal_mapping = {'Exam preparation': 0, 'Assignments': 1, 'Concept review': 2, 'Project work': 3, 'Homework': 4, 'Practice': 5}
goals_encoded = array([[2], [4], [3], ..., [0], [0], [0]])
features = array([[ 1. , 1. , 0. , ..., 0. , 17. , 2. ], [ 0. , 1. , 1. , ..., 0. , 6.5, 4. ], [ 0. , 0. , 0. , ..., 0. , 11.5, 3. ], ..., [ 0. , 0. , 0. , ..., 0. , 15.5, 0. ], [ 1. , 0. , 0. , ..., 0. , 14.5, 0. ], [ 1. , 0. , 0. , ..., 0. , 14.5, 0. ]])
imputer = SimpleImputer()
features_imputed = array([[ 1. , 1. , 0. , ..., 0. , 17. , 2. ], [ 0. , 1. , 1. , ..., 0. , 6.5, 4. ], [ 0. , 0. , 0. , ..., 0. , 11.5, 3. ], ..., [ 0. , 0. , 0. , ..., 0. , 15.5, 0. ], [ 1. , 0. , 0. , ..., 0. , 14.5, 0. ], [ 1. , 0. , 0. , ..., 0. , 14.5, 0. ]])
scaler = StandardScaler()
features_scaled = array([[ 1.47562862, 1.48098189, -0.67240404, ..., -0.67293718, 0.97078858, -0.28674104], [-0.67767729, 1.48098189, 1.48720105, ..., -0.67293718, -1.42462074, 0.88751054], [-0.67767729, -0.6752277 , -0.67240404, ..., -0.67293718, -0.28394964, 0.30038475], ..., [-0.67767729, -0.6752277 , -0.67240404, ..., -0.67293718, 0.62858725, -1.46099262], [ 1.47562862, -0.6752277 , -0.67240404, ..., -0.67293718, 0.40045303, -1.46099262], [ 1.47562862, -0.6752277 , -0.67240404, ..., -0.67293718, 0.40045303, -1.46099262]])
kmeans = KMeans(n_clusters=3, random_state=42)
def find_matching_students(new_student, student_df, kmeans, scaler, mlb, n_matches=3):
 86def find_matching_students(new_student, student_df, kmeans, scaler, mlb, n_matches=3):
 87    """
 88    Find the top matching students for a new student based on their study profile.
 89
 90    Args:
 91        new_student (dict): The new student's data (name, subjects, study hours, goals).
 92        student_df (pd.DataFrame): DataFrame containing the student data.
 93        kmeans (KMeans): Trained KMeans model to predict the cluster for the new student.
 94        scaler (StandardScaler): Pre-trained scaler to standardize the features.
 95        mlb (MultiLabelBinarizer): Pre-trained MultiLabelBinarizer to encode subjects.
 96        n_matches (int): The number of closest matching students to return (default is 3).
 97
 98    Returns:
 99        pd.DataFrame: The `n_matches` closest students from the same cluster.
100    """
101    # Process new student data
102    new_subjects = mlb.transform([[s.lower() for s in new_student["subjects"]]])[0]
103    new_avg_hour = np.mean(new_student["study_hours"])
104    new_goal_encoded = goal_mapping[new_student["goals"]]
105
106    # Create a feature vector for the new student
107    new_student_features = np.hstack([new_subjects, new_avg_hour, new_goal_encoded]).reshape(1, -1)
108    new_student_features_scaled = scaler.transform(new_student_features)
109
110    # Predict the cluster for the new student
111    cluster = kmeans.predict(new_student_features_scaled)[0]
112
113    # Filter the students to those in the same cluster
114    cluster_students = student_df[student_df["cluster"] == cluster]
115    cluster_features = features_scaled[student_df["cluster"] == cluster]
116
117    # Calculate pairwise Euclidean distances to find the closest matches
118    distances = pairwise_distances(new_student_features_scaled, cluster_features, metric="euclidean").flatten()
119    closest_indices = distances.argsort()[:n_matches]
120
121    # Return the closest matching students
122    return cluster_students.iloc[closest_indices]

Find the top matching students for a new student based on their study profile.

Args: new_student (dict): The new student's data (name, subjects, study hours, goals). student_df (pd.DataFrame): DataFrame containing the student data. kmeans (KMeans): Trained KMeans model to predict the cluster for the new student. scaler (StandardScaler): Pre-trained scaler to standardize the features. mlb (MultiLabelBinarizer): Pre-trained MultiLabelBinarizer to encode subjects. n_matches (int): The number of closest matching students to return (default is 3).

Returns: pd.DataFrame: The n_matches closest students from the same cluster.

new_student = {'name': 'KULDEEP', 'subjects': ['math', 'biology'], 'study_hours': [12, 17], 'goals': 'Exam preparation'}
kmeans_model = KMeans(n_clusters=3, random_state=42)
matching_students = name subjects study_hours goals cluster 50002 KULDEEP [math, biology] [12, 17] Exam preparation 1 11820 Reyansh [biology, math] [14, 15] Exam preparation 1 12516 Divit [math, biology] [14, 15] Exam preparation 1 5019 Vihaan [biology, math] [13, 16] Exam preparation 1 35003 Dev [math, biology] [14, 15] Exam preparation 1